Sergio Jiménez

A Survey of the Seventh International Planning Competition

In this article we review the 2011 International Planning Competition. We give an overview of the history of the competition, discussing how it has developed since its first edition in 1998. The 2011 competition was run in three main separate tracks: the deterministic (classical) track; the learning track; and the uncertainty track. Each track proposed its own distinct set of new challenges and the participants rose to these admirably, the results of each track showing promising progress in each area. The competition attracted a record number of participants this year, showing its continued and strong position as a major central pillar of the international planning research community.

Review: a review of machine learning for automated planning

Recent discoveries in automated planning are broadening the scope of planners, from toy problems to real applications. However, applying automated planners to real-world problems is far from simple. On the one hand, the definition of accurate action models for planning is still a bottleneck. On the other hand, off-the-shelf planners fail to scale-up and to provide good solutions in many domains. In these problematic domains, planners can exploit domain-specific control knowledge to improve their performance in terms of both speed and quality of the solutions. However, manual definition of control knowledge is quite difficult. This paper reviews recent techniques in machine learning for the automatic definition of planning knowledge. It has been organized according to the target of the learning process: automatic definition of planning action models and automatic definition of planning control knowledge. In addition, the paper reviews the advances in the related field of reinforcement learning.

Scaling up heuristic planning with relational decision trees

Current evaluation functions for heuristic planning are expensive to compute. In numerous planning problems these functions provide good guidance to the solution, so they are worth the expense. However, when evaluation functions are misguiding or when planning problems are large enough, lots of node evaluations must be computed, which severely limits the scalability of heuristic planners. In this paper, we present a novel solution for reducing node evaluations in heuristic planning based on machine learning. Particularly, we define the task of learning search control for heuristic planning as a relational classification task, and we use an off-the-shelf relational classification tool to address this learning task. Our relational classification task captures the preferred action to select in the different planning contexts of a specific planning domain. These planning contexts are defined by the set of helpful actions of the current state, the goals remaining to be achieved, and the static predicates of the planning task. This paper shows two methods for guiding the search of a heuristic planner with the learned classifiers. The first one consists of using the resulting classifier as an action policy. The second one consists of applying the classifier to generate lookahead states within a Best First Search algorithm. Experiments over a variety of domains reveal that our heuristic planner using the learned classifiers solves larger problems than state-of-the-art planners.

INTEGRATING PLANNING, EXECUTION, AND LEARNING TO IMPROVE PLAN EXECUTION

Algorithms for planning under uncertainty require accurate action models that explicitly capture the uncertainty of the environment. Unfortunately, obtaining these models is usually complex. In environments with uncertainty, actions may produce countless outcomes and hence, specifying them and their probability is a hard task. As a consequence, when implementing agents with planning capabilities, practitioners frequently opt for architectures that interleave classical planning and execution monitoring following a replanning when failure paradigm. Though this approach is more practical, it may produce fragile plans that need continuous replanning episodes or even worse, that result in execution dead‐ends. In this paper, we propose a new architecture to relieve these shortcomings. The architecture is based on the integration of a relational learning component and the traditional planning and execution monitoring components. The new component allows the architecture to learn probabilistic rules of the success of actions from the execution of plans and to automatically upgrade the planning model with these rules. The upgraded models can be used by any classical planner that handles metric functions or, alternatively, by any probabilistic planner. This architecture proposal is designed to integrate off‐the‐shelf interchangeable planning and learning components so it can profit from the last advances in both fields without modifying the architecture.

Automating the evaluation of planning systems

Research in automated planning is getting more and more focused on empirical evaluation. Likewise the need for methodologies and benchmarks to build solid evaluations of planners is increasing. In 1998 the planning community made a move to address this need and initiated the International Planning Competition --or IPC for short. This competition has typically been conducted every two years in the context of the International Conference on Automated Planning and Scheduling ICAPS and tries to define standard metrics and benchmarks to reliably evaluate planners. In the sixth edition of the competition, IPC 2008, there was an attempt to automate the evaluation of all entries in the competition which was imitated to a large extent and extended in several ways in the seventh edition, IPC 2011. As a result, a software for automatically running planning experiments and inspecting the results is available, encouraging researchers to use it for their own research interests. The software allows researchers to reproduce and inspect the results of IPC 2011, but also to generate and analyze new experiments with private sets of planners and problems. In this paper we provide a gentle introduction to this software and examine the main difficulties, both from a scientific and engineering point of view, in assessing the performance of automated planners.

Machine learning of plan robustness knowledge about instances

Classical planning domain representations assume all the objects from one type are exactly the same. But when solving problems in the real world systems, the execution of a plan that theoretically solves a problem, can fail because of not properly capturing the special features of an object in the initial representation. We propose to capture this uncertainty about the world with an architecture that integrates planning, execution and learning. In this paper, we describe the PELA system (Planning-Execution-Learning Architecture). This system generates plans, executes those plans in the real world, and automatically acquires knowledge about the behaviour of the objects to strengthen the execution processes in the future.

Computing Hierarchical Finite State Controllers With Classical Planning

Finite State Controllers (FSCs) are an effective way to compactly represent sequential plans. By imposing appropriate conditions on transitions, FSCs can also represent generalized plans (plans that solve a range of planning problems from a given domain). In this paper we introduce the concept of hierarchical FSCs for planning by allowing controllers to call other controllers. This call mechanism allows hierarchical FSCs to represent generalized plans more compactly than individual FSCs, to compute controllers in a modular fashion or even more, to compute recursive controllers. The paper introduces a classical planning compilation for computing hierarchical FSCs that solve challenging generalized planning tasks. The compilation takes as input a finite set of classical planning problems from a given domain. The output of the compilation is a single classical planning problem whose solution induces: (1) a hierarchical FSC and (2), the corresponding validation of that controller on the input classical planning problems.